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Accurate SPARQL generation via in-context learning and schema-based query construction.

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Summary

This study introduces a novel framework for generating SPARQL queries from natural language questions, overcoming limitations of existing large language models by avoiding hallucinations and eliminating the need for training data. The approach enhances biological data accessibility through an intuitive, schema-driven query builder.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Integrated analysis of biological databases is crucial for life science research.
  • Semantic Web technologies and knowledge graphs are increasingly adopted by public biological databases.
  • Complex RDF schemas pose challenges for non-expert users in querying biological data.

Purpose of the Study:

  • To develop a novel framework for automatic SPARQL query generation from natural language questions.
  • To overcome limitations of current large language model (LLM)-based approaches, specifically structural hallucinations and the need for large training datasets.
  • To improve accessibility of biological data resources for researchers.

Main Methods:

  • Combines LLM-based word extraction with a schema-based SPARQL query builder.
  • Utilizes predefined schemas in prompts to guide the LLM, eliminating the need for training data.
  • A proof-of-concept chatbot system was developed for natural language querying of RDF databases.

Main Results:

  • The proposed method generates syntactically correct SPARQL queries by extracting variables and parameters based on a predefined schema.
  • Experimental results on UniProt, Rhea, and Bgee demonstrate superior performance compared to baseline LLM methods.
  • The approach achieves higher similarity between results from generated and expert-written queries, outperforming fine-tuning and prompt-tuning methods.

Conclusions:

  • The novel framework effectively generates accurate SPARQL queries without requiring training data or suffering from structural hallucinations.
  • The developed chatbot system showcases practical utility in enhancing user access to complex biological data.
  • This approach significantly improves the ability of non-expert users to query and analyze biological knowledge graphs.